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Rehabilitation of gait function in post-stroke hemiplegic patients is critical for improving mobility and quality of life, requiring a comprehensive understanding of individual gait patterns. Previous studies on gait analysis using unsupervised clustering often involve manual feature extraction, which introduces limitations such as low accuracy, low consistency, and potential bias due to human intervention. This cross-sectional study aimed to identify and cluster gait patterns using an end-to-end deep learning approach that autonomously extracts features from joint angle trajectories for a gait cycle, minimizing human intervention. A total of 74 sub-acute post-stroke hemiplegic patients with lower limb impairments were included in the analysis. The dataset comprised 219 sagittal plane joint angle and angular velocity trajectories from the hip, knee, and ankle joints during gait cycles. Deep temporal clustering was employed to cluster them in an end-to-end manner by simultaneously optimizing feature extraction and clustering, with hyperparameter tuning tailored for kinematic gait cycle data. Through this method, six optimal clusters were selected with a silhouette score of 0.2831, which is a relatively higher value compared to other clustering algorithms. To clarify the characteristics of the selected groups, in-depth statistics of spatiotemporal, kinematic, and clinical features are presented in the results. The results demonstrate the effectiveness of end-to-end deep learning-based clustering, yielding significant performance improvements without the need for manual feature extraction. While this study primarily utilizes sagittal plane data, future analysis incorporating coronal and transverse planes as well as muscle activity and gait symmetry could provide a more comprehensive understanding of gait patterns.

In this study, we conduct a pioneering and comprehensive examination of ChatGPT’s (GPT-3.5 Turbo) capabilities within the realm of Korean Grammatical Error Correction (K-GEC). Given the Korean language’s agglutinative nature and its rich linguistic intricacies, the task of accurately correcting errors while preserving Korean-specific sentiments is notably challenging. Utilizing a systematic categorization of Korean grammatical errors, we delve into a meticulous, case-specific analysis to identify the strengths and limitations of a ChatGPT-based correction system. We also critically assess influential parameters like temperature and specific error criteria, illuminating potential strategies to enhance ChatGPT’s efficacy in K-GEC tasks. Our findings offer valuable contributions to the expanding domain of NLP research centered on the Korean language.

Dialogue relation extraction identifies semantic relations between entity pairs in dialogues. This research explores a methodology harnessing the potential of prompt-based fine-tuning paired with a trigger-generation approach. Capitalizing on the intrinsic knowledge of pre-trained language models, this strategy employs triggers that underline the relation between entities decisively. In particular, diverging from the conventional extractive methods seen in earlier research, our study leans towards a generative manner for trigger generation. The dialogue-based relation extraction (DialogeRE) benchmark dataset features multi-utterance environments of colloquial speech by multiple speakers, making it critical to capture meaningful clues for inferring relational facts. In the benchmark, empirical results reveal significant performance boosts in few-shot scenarios, where the availability of examples is notably limited. Nevertheless, the scarcity of ground-truth triggers for training hints at potential further refinements in the trigger-generation module, especially when ample examples are present. When evaluating the challenges of dialogue relation extraction, combining prompt-based learning with trigger generation offers pronounced improvements in both full-shot and few-shot scenarios. Specifically, integrating a meticulously crafted manual initialization method with the prompt-based model—considering prior distributional insights and relation class semantics—substantially surpasses the baseline. However, further advancements in trigger generation are warranted, especially in data-abundant contexts, to maximize performance enhancements.

Machine reading comprehension is a question answering mechanism in which a machine reads, understands, and answers questions from a given text. These reasoning skills can be sufficiently grafted into the Korean College Scholastic Ability Test (CSAT) to bring about new scientific and educational advances. In this paper, we propose a novel Korean CSAT Question and Answering (KCQA) model and effectively utilize four easy data augmentation strategies with round trip translation to augment the insufficient data in the training dataset. To evaluate the effectiveness of KCQA, 30 students appeared for the test under conditions identical to the proposed model. Our qualitative and quantitative analysis along with experimental results revealed that KCQA achieved better performance than humans with a higher F1 score of 3.86.

In an age overflowing with information, the task of converting unstructured data into structured data are a vital task of great need. Currently, most relation extraction modules are more focused on the extraction of local mention-level relations—usually from short volumes of text. However, in most cases, the most vital and important relations are those that are described in length and detail. In this research, we propose GREG: A Global level Relation Extractor model using knowledge graph embeddings for document-level inputs. The model uses vector representations of mention-level ‘local’ relation’s to construct knowledge graphs that can represent the input document. The knowledge graph is then used to predict global level relations from documents or large bodies of text. The proposed model is largely divided into two modules which are synchronized during their training. Thus, each of the model’s modules is designed to deal with local relations and global relations separately. This allows the model to avoid the problem of struggling against loss of information due to too much information crunched into smaller sized representations when attempting global level relation extraction. Through evaluation, we have shown that the proposed model yields high performances in both predicting global level relations and local level relations consistently.